Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A broad spectrum of space application domains are increasingly making use of heterogeneous and large volumes of data with varying degrees of human in the loop. Applications include critical areas such as space habitat management, healthcare delivery, and emergency management while the International Space Station Environmental Control and Life Support System (ECLSS) as well as PHM for Human Health and Performance (PHM4HHP) serve as examples of the applications. This paper suggests a discussion on implementation of the Model Driven Engineering paradigm (a.k.a. Model Based Systems Engineering approach) for PHM focusing on HHP on crewed space exploration missions and introduces a conceptual model and framework - the PHM4HHP Domain Specific Language - to support a data-centric and model-driven approach and develop requirements for integration of heterogeneous models and their respective data for the entire life-cycle of the Health Support System (HeSS) being designed to validate on the International Space Station (ISS). While the discussion starts with focusing on PHM4HHP concepts that make the PHM4HHP domain different from conventional healthcare delivery and is to elaborate the PHM foundations for HHP in terms of basic concepts, driving principles, and current practices to employ PHM4HHP on space exploration missions, the second part is an effort to convert the PHM language to a UML-based metamodel in terms of the promising model-driven and data-centric approach including inherent exercises such as profiling, mapping, and building metamodels. The paper also articulates key requirements in terms of predictive diagnostics providing early and actionable real-time warnings of impending health issues that otherwise would have gone undetected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it